 This research paper proposes a novel approach to determine the optimal number of automatic guided vehicles, AGVs, needed for a given production line. The approach uses a genetic algorithm, GA, to identify the best AGV configuration for a given production line. The GA is then used to optimize the number of AGVs based on the production requirements. Additionally, the paper explores the use of artificial neural networks, ANNs, and multiple linear regression, MLR, to predict the effects of changing system parameters on the number of AGVs. The results demonstrate that the ANN provides solutions which can be used in workshops to determine the number of AGVs and also to predict the effect of changes in system parameters. This article was authored by Onur Masoot Senares, Errol Salmaz, Nersil Osterk, and others.